{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "# Plot the support vectors in LinearSVC\n",
        "\n",
        "Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide\n",
        "the support vectors. This example demonstrates how to obtain the support\n",
        "vectors in LinearSVC.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      },
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qsekNJXAFmUqOQRGIj5gEFQEYx9HsoDcQQWxE1zBNAsf9YUTiGizG8V1zxlQNr+zrgy8cHz6GxKxqJ85tKGP5QiKa9oyKyFio0D83jjnww75AYvU5hapJ7O4NoM5lHfc4jvvD2LC/X/9YCGDlzGpUOthnodRxBZlKTmeVEyNjYSEE2sodp31uXJUYbeqeyMrD9mM+DIZiiGsScS2xArL3ZBC9gci4j0FEVKxqXBa96UySIoBalxXmcSw0xFQt6+PRUR7PRtUkXt/fr8/DcU0ipkps6D4Jma2vOpUUBshUcubXuTCz0gFFJCZkm8mAS9srYTcbTvvcGpcF2eJgh9kIm2n8P04HTw1lHEfVJHpOnT7Ng4io2ClC4PLhDXnJubjOZcWytopxPb+13JZR8cIggLZy+7jH0B+MZl3FjqlSv7tHpYspFlRyFCFwfnM5FjV6EFM1WI3KuNMazAYFF7aW462eAf05igCWz6iYUGrEyJUTING61zSONA8iounAbTXhmjm1iMRVKEJMaKNzR5UTx3wRnPBHIERin1+t04KZVae/E5hkGGW+lVKOK+WOpjcGyFSyjIqAUTn9qvFILeV21Lqseu3O+jOo3TmrxoltR3xpOXSKEJhZOf7JnYhoOrAYJz4PK0JgxcwqDAxF4Q3FUGYzjasKUaoKmwlWo4JgVNVXkgUAj4218YkBMtEZsRgVtE7gVt5Is6qcCEVV7OkLApAwGhQsaSmH2zr++p9ERKWuwm6ecGCcJIbrLr/e3Y/BUKJsZ5XDjOVtlbkcIhUpBshUtJLd8JKrB9UOc9FUgBBC4NxGDxbUlyGqarBNIM2DiGgqiakajnjDiGsaGspssJsmviJcKA6zEVfNTqR5CIhxbRCk0sAAmYqSqkm8+tFJDAxFoUkJRQiU20xY2VFdVK1KzzTNIxspJWKahEkRDLaJKC/6g1H8cV8fJBJ5wFsPD6KruRztRZYudiZpHqNRNTmcx8xgu5gxQKaitO9kAP3BqJ7Dq0mJgaEo9p4MYE6Nq8Cjy7+egSC2HPEiqmowKQoWNbjZUYqIJpWUEm8c6EdsREmedw6dQmOZbdx14aeLuKZh08FTODQYgpRAhd2Ei9oq4bIw1CpGpfW/l6aNg4OhzCLxEjhYgmXS+gIRvH1wEJG4BikTdUC3HvbimG/iHaWIiMYrFNMQytL1ThGiJGu6v9WTCI614e7Z/UMxvLynFxprKhclBshUlEZbmSi1FQsA2J2to5SU+KDXX6AREVEpGKsUmrnEyqQl8rBDGfXt45rECX/pXSxMB6UXTdC0MKfGBcOIPFuDIjC3BNMrovHsnaNiozxORJQLZoOCRrcNqds+BBILFdVOS8HGVQhjdVKdSHc/mjoYIFNRqnFasLTFk2jyAcBqVLCk2YMaV2lNygDQWm7PvFgQQEu5rUAjIqJScWFbOVrL7VBEIjiudppxeWc1lBLbKGw1GeDMUjtZSom6Evy9NB0wc5yKVmuFAy3ldqgyERCWauWGGZV2HPaGcCIQASQgRKIA/qzq0ltNJ6L8MioKLmytwJKWckiJoqoilGvLZlTglb19epqFlBJdzeU5rZBB+cMAmYqaEALG0p2PAXzcUao/GMVgKAa31YiqIqoJTUTFTxHDS8glrNxmxvULGnDUG0Jck6h3W2EroprQlI4BMtE0Uekwo9JxZh2liIjo7BkVgZaz6LJKUwdzkImIiIiIUjBAJiIiIiJKwRQLohIUiWs45gtDCKDBbYWJLVGJiPJuYCiKU6EY3BbuHZlqGCATlZgj3hDe6B5A6jx82cyqkqtbSkRUKJqUeKN7QO94KgTgsZmwsqMaxhKuBDKVMEAmykJKid29Aew84UdU1VBpN+GC5nKU24t7E1xck3jzwECi815KXfvXu/vx5wvq87p6EVc19AWjMBsUVNhNXDkhogyhmIp3Dp3CUW8YiiLQXmHHeY2eoi8n13NqCMf84Y+7oErg1FAUu3v9mF/nzutY/JE4/OEYPHYz7Ky6oWOATJTF3pNBbD/u07sj9Q/FsH5vH9bOryvqmpYng9lbnsY1CV84jjKbKS/jOHRqCG8dPAWBRJxuMxmwqqMadnPxfm+JKLeklFi/tw+BSBwSiW51+/uDiKoalrVVFnp4Z+XgqaGM7nuqTATO+QqQP17FDkERAqqU6Kxy4rzGMi5YgJv0iLL64IQ/Y/LSpMSBgaECjSg3zKPkGkspYTLkZ0KMxFVs7DmFuCYR0yTimkQgEsfGnv68vD4RFYeTwShCMTX1ZhdUCRwaDBV9+2azIftiwGhz9GTY3RvAMV8YqgRimoQmgX39QRwdTvsodQyQibLINvmqEgjHi3tSLreZYDcZ0ur5KyJRQ9mepU3qZDjqDWPk4oQE0BeIIj7iooSIStdo862AQKzIA+RZ1U4YRkyEBkVgTm3+OqB2DwQ/TvEYpmoS3f3BvI1hKmOATEUnHFOxpy+AXcd98IZik/Iata7MDWsGRaDeZZ2U18sXIQRWdlaj2mmGQGJjSL3LiktmVOV1DNk/UfKNuIiKhpQSJ4MRvH/ch4/6g5MSsFY7zNBk5kWz2SiKPle20mHGha3lsBoVKAIwGQTObShDU5ktb2NQRpmLlSLP786VnC0ZCSEMADYDOCKlvDZXxyVK1RuI4E8fnYSUidtB7x/3Y0G9G/NyfNV9fpMH/cFexDUJVZNQhECLx4ZqZ3Fv0gMAu8mAyztrEFc1QIi875huLLPinUPpjynD5eaKfePNVMC5mCablBJvHzyFg4MhqJqEQRF474gXV86qhsuau30MVpMBixs9ePfIICQSK8dCAMvaKqdFjmxLuR3NHhtiqoTRIEYNWCdLZ5UDWw5701aRDUKgo8qZ13FMVbm8p/plAB8AyO/2SyoZUkpsPDCQdhtelRI7jnnRVmHP6YqCw2zE2vn1ODwYwlBMRa3TMu3aOBsLVPvYZFBw2cwqvHGgH1FVQkqJaqcFS1srCjKeaYhzMU2qk8GoHhwDidvyKiQ2Hx7Eyo7qnL5WZ7UTdW4rjnhDMCoCzR5bUW+UHkkIAbOxMMF+e6UDvkgce/sCEEJAAji3oQw1LPkJIEcBshCiCcAaAP8A4MFcHJNopHBcQziuZjyuCIG+QASt5facvp5REWiryO0xKaHaacH18+vhj8RhMiiwFfnt0qmCczHlwwl/JGMTM5C4wzcZXBYj5tTkLze3VAghcF6jBwvq3BiKqnBYjKzBnCJXS0iPA/gagFGTkIQQ9wohNgshNvf19eXoZamUjFVlwWpkOn2xEULAbTUxOM4tzsU06awmJWs6VD4rMFDumAwKymwmBscjnPX/ZiHEtQB6pZRbxvo6KeWTUsouKWVXdXVub8FQaTAqCmZU2NN2/gokaujylhCVOs7FlC8tHjtGrlcYFIF5dVzlpekjF5d7ywFcJ4Q4AOBZAKuEEP+Vg+MSZehqLsesagdMBgFFAI1lNlzeWT0tNmwQnSXOxZQXZqOC1bNqUOM0QxGJO3iL6t2Yxc1dNI0ImaWEyhkfTIjLADx0up3TXV1dcvPmzTl7XSKiYiOE2CKl7JqkY18GzsVERKc12lzMhCEiIiIiohQ5bZ0lpXwVwKu5PCYREU0M52IiorPDFWQiIiIiohQMkImIiIiIUjBAJiIiIiJKwQCZiIiIiCgFA2QiIiIiohQMkImIiIiIUjBAJiIiIiJKwQCZaJqLqhqOekPoD0aRy86ZREQ0ft5QDEe8IYRiaqGHQuOQ00YhRDS17O8PYvOhQQiR+NhhNmBVRzWsJkNhB0ZEVCJUTeK1/SfRF4hCEYAqJebWuLCwoazQQ6MxcAWZaJoKRuPYfOgUVCkR1xJ/fOE4Nh06VeihERGVjJ3HfegLRKBKiZgmoUngw74ATvjDhR4ajYEBMtE0dcSbOflKAEe9YaZaEBHlSfepIagjplxVk+g5FSrMgGhcGCATTVOKAPTcihRZHiIiokmijDLpKpyLpzTmIBNNkmO+MN47OohAREWZ1YjFTR5UOSx5e/1mjw1bj3jTHlME0FZuh2CUTEQlQNUk3j/uxUcnh6BBotVjx7mNZTAZ8rc+2FnlwPZjPqjax8vIBiEws9KRtzHQxHEFmWgSnAxGsGF/PwZDccQ1if6hGF7ZdxK+cCxvY7AYDVjRXgmrUYFBEVAEUO+24vxmT97GQERUSG8fHMDu3gAiqoaYKrF/IIg/fXQyr2OYXe1Ee4UdigCMioBJEbig2YNyuzmv46CJ4Qoy0STYedwPdUSer6ZJfNgbwJKW8ryNo9ZlxZ8vqIc/EofZoJRE9YpgMIiXX34ZXq8X9fX1WLlyJYxGTnVEpSYSV3FoMISUhVtoEhgYisEbiqHMZsrLOIQQ6Goux8KGMoRjKhxmIwzMr5jy+FuDaBIEo/GMxySAQCTz8ckmhIDbmp9fBIUUjUbxyCOP4Omnn8bixYtRW1uLPXv24NixY3j44YfxhS98oSCpJVJKDA0Nwe/3IxAIwO/3IxTi5hyiyRaOaVCEgDZisUIRwFBMzVuAnGQ2KDDnMbWDzg4DZKJJUO+2wh8JpK1cGATQWGYt3KCmsXg8jhtuuAEmkwlbt25FU3MLInENVpOCbe+9hzvvvBNHjhzBY489lvPX9nq9OHHiRFoA7Pf7cfPNN8NkMuEPf/gDNm7cmPPXJaKxuazGrJuSNSlRyfSGvIjENQCAxVh8FwYMkIkmwbxaNw4NhhCOa1A1CaMi4DQbMbOKmzImw49+9CMEAgG8/PLL+PDkEJ7ffhQAYFAEutpmY/369Tj33HNx4403oqura8xjxeNxBAIBOBwOmEwmnDhxAjt37kwLfgOBAO666y5UVlZi165d+P3vf68/3263w+VyIRwOw2QyYc6cOfB4PHC5XHA6nfrff/d3fzep3xOiUqcIgSXN5Xir55S+iqwIgXMb3TAXYcBWTIZiKt7o7sfAUBQAUGE3Y/mMStiLKM2PATLRJLAYFfzZ3DocPDUEbyiGSocZTR7bqOV+6MxJKfHEE0/gO9/5Dg56I/igN6Dnf6uqxNs9p7B6VjXuv/9+/Ou//isee+wxBAIBNDY2wuPx4OjRo1i/fr0e+A4NDQEA7rjjDsyYMQMnT57Ehg0b4HQ64XQ64Xa70dDQAIMhMdHPnz8fLS0t+ueTjye1traitbU1v98UIgIAtJTbUWE348DAEDQp0VJuhyfPqRWlRkqJV/f1wReOI3kTtT8Yxav7+nDNnNqiqaLEAJlokhgVgXaW8Zl0g4OD2Lt3L9rb2/H7N95B3ykvwkNB1Le2o7qhGQN9J/DIz38IERzA7373O3R2dgIAbrjhBng8HgghEA6HUVFRgdbWVn2Vt7KyEgAwZ84cPProo1CU7CtObrcbbrc7b+dLRBPjtBixoJ4/o/nii8QRiKhIzfxO7MFR4QvH8577faYYIBPRlCOlhKqqMBqNUFUVO3bsyEhxOOecc3DBBRfA6/VC0zT85Cc/we5eP6KqhNFohN3lRnVDMywOJ9rnzkeLQ8Hzzz+P22+/HU6nE+XliWoi9fX1uOeee0Ydy8gVYSIiGl1ck4nc7xHdA4VIfK5YMEAmorzRNE2v6KAoCmprawEA69atw+DgoB4EBwIBnHvuuVi7di2EEPjtb38LTdNgsVj0Fd5k6bampia4XC5ceOGFuKyxE4eGJAwmi34bz+lw4MobrsP63zyH888/HzNnzizY+RMRTXflNhMMQiA+IkI2CIFye3GsHgMMkIkoB1RVRSAQSAtwTSYTFi1aBAD4+c9/jqNHjyIQCEAO5wd3dnbitttuAwAcOHAAmqbB6XSira0NTqcTLS0tAABFUfClL30JDocDZnPmznOj0YgvfOELeO655/DDp/4dr+7rw0AoBoHESnRbuR01diO+973v4Rvf+EZevh9ERKVKEQKXtFfi1RENWS5pryyqfTgMkInotHp7e3Hy5Mm0UmYGgwFr164FADzzzDPo6elJe05tba0eIFdUVMDhcKRVcaioqNC/9r777hvz9ZPpEKN54IEHsHTpUvzzd76Nhx56CIMRFYFIHBV2M0wyjrvuugtVVVW45pprzuT0iYhoAqqdFtxwTj2O+yMAgDqXBcZR9nFMVQyQiUpQJBJBIBDQN6Lt27cP+x3+HiUAACAASURBVPfvT1sBjsVi+MpXvgIA2LBhA3bs2AEgsaLrdDpRXV2tH2/p0qVYuHBhWgDsdDr1z1911VWTej4VFRV45ZVXcMstt+DJJ5/EHXfcoTcKeeaZZ3DFFVfg+eefZz4xEVGeGBUFTWW2Qg/jjDFAJpompJQIh8Npm9nmzZsHk8mEHTt2YPPmzXrwG40malP+9V//NSwWC7q7u7Fp0yY9wK2qqoLL5YKmaVAUBStWrMDy5cvhcrlgt9szyvTMmzevEKecprm5GW+++SY2bdqEX/3qVzh8+DDq6+uxceNGdHR0FHp4RERURBggExWBaDSKgYGBtBSHQCCAZcuWwePx4L333sP//M//IB5Pb2Xd0NCA6upqaFqim1F9fT1cLpe+ypssXbZq1SpcccUVo9anrKqqmtwTzBEhBJYuXYqlS5cWeihERFTEGCATFYimadA0DUajEYFAALt3704rY+b3+3HllVeira0N+/fvx7PPPpv2fKvVigULFsDj8aC6uhpLlixJ69bmcrng8XgAAIsWLdLzgbNh6gEREdHHGCAT5ViyVbHf79fr7QYCgbRubX6/H0NDQ1i7di0WL14Mn8+HF198EQDSNrMlV3Sbmppwyy23pOX4mkwfl8tpbGxEY2NjQc6XiIhoumGATDRO0Wg0Lb3B7/ejuroaM2fORCQSwdNPPw2/349QKKQ/57LLLsNll10GIQT27duntypubGyE0+lEXV0dgETFhwcffBAOhyPraq7T6ZwSeb5ERESl4KwDZCGEFcBrACzDx/uVlPL/nu1xifKtp6cHXq83LQiura3FxRdfDCklvv3tb2fk+C5evBgzZ86E2WxGVVVVWqtil8ulV3pwOBz46le/OuprGwwGtiums8K5mIgod3KxghwBsEpKGRBCmAC8LoR4SUr5Vg6OTXRGpJQIhUKIRCJ6Dd2tW7fixIkTaSvANTU1+NSnPgUA+M1vfoOBgQEAgMlk0lMZgMTmr6uuuirtcZfLBZvNpn/+lltuKcCZEuk4FxMR5chZB8gy0RYrMPyhafhP8TTbpqKiaRqCwaAe4EajUSxYsAAA8Oqrr2Lfvn16AKyqKqqqqvCXf/mXAIDt27fj2LFjeoDb1NSE+vp6/dif+MQn9ADYbDZnVHS44IIL8neiRBPEuZiIKHdykoMshDAA2AKgA8D3pZRvZ/maewHcC0BvIUuUamhoCAMDA2kpDsFgEGvWrIEQAi+99BI2bdqktyoGALPZjPnz50MIAVVVYbFYUFlZqQfBySoOAHD77bePWa0hNVgmGs0777yDH/3oR+ju7obD4cC1116LT37yk/rdhELiXExElBsiNdg464MJ4QHwawBfklK+P9rXdXV1yc2bN+fsdSm3Dg0O4YMTfkRViZZyG+bVuGA0nFmLyFgsBoPBAEVR0Nvbi+7u7oxavnfccQfsdjteeeUVvPbaa/pzhRBwOBx44IEHYDab8cEHH6StACf/LisrG7V+L1GuBAIB3Hbbbdi2bRvuu+8+LFy4EIODg/jpT3+KLVu24LnnnsMll1wy7uMJIbZIKbsmY6yci4tfNK5h5wkfjnjDsBoVzKt1oaGIu5IRTVWjzcU5rWIhpRwUQrwK4GoAo07KNHXt7vVj2zEfVC1x4fTBCT+O+yJYPataD0KllIhGo3qAW1NTA7vdjsOHD2PTpk1ptXzD4TDuu+8+1NXV4eDBg3jppZf0VsXJOr2qqgIAFixYgKamJv1zDodDb2QBAHPnzsXcuXPz/02hkielxK233orKykrs3bsXvphErz8Cm8mAWz75Kfxx/cu46aabsH79epxzzjmFHi7n4iKnSYk/7OlFMBqHJgF/BHi9ewBLWjxoq3AUenhEU4KqSRwaDCEUU1HjtKDSYc7p8XNRxaIaQGx4QrYBuALA/3fWI6O8UzUNm7tPwB8IIBwMIBwaQigYQFNbO/oayxAd7MMLL7yQ1qoYAD796U9j1qxZCIfDOHToEJxOJ2pqatDe3q63JgaAc845B3Pnzs3aqhgAampqUFNTk7fzJRqv119/HR999BH++7//G+8eC+DAwBA0KWFQBN49MogrV6zEww8/jMceeww///nPCzJGzsXTxxFvGKGYCi3lBq8qJd476mOATAQgGI3jD7t7EdckVE1CUQRaPDYsbSkf9Y5yMBiEz+dL+5MstZpNLlaQ6wH853DumwLgl1LK/8nBcSnH4vE4uru79fSG5CrvwoULMXfuXBw9dgK//vETGJl1Y7Pa4I90otxuR0NDQ0aKQzJ3t6OjA1/+8pdHfX2LxQKLxTKZp0g0KZ566incf//9OBXRcODUENThH5Lk5Lzl0CA+97nP4Zvf/Cb6+/tRWVlZiGFyLp4m/JG4fhcvVSimFmA0RFPP1sNehOOa/rGqSWzbvR+REwrMagQ+n0/vVbBixQoAwPe+9z1EIhH9OYqijLn5PhdVLLYDOO9sj0MTp2kaAoHEpnW32w1N0/Daa6+l5fj6/X6cd955WLVqFeLxOH7605/qz7darXC5XAiHwwCA6spydF28EorFBqvdCavdAZvDAYvZggqbCeV2B26++eaCnCtRIe3btw/33nsvjvnCGYGLBHA8EMGlMxN1sA8dOlSQAJlz8fRRYTfBoAjER/xfc1vY24tKi6qq+ub6PXv26KVa/7DjAAJ+P+xOFy666joAwFuvrMPWaAD1bitMJhPcbrdeqhUA1qxZA7PZDLfbnTWNcyT+tE1Bqa2KA4EAzGYzZs6cCQB47rnn0N/fr7cqllJi4cKFuPHGGyGEwJtvvplWq7e2tla/hWCxWPD5z38eTqczo1UxkAiYb1tzOTb2nIImJSQAoyLQ4Lai3J7b3B6iYmK1WhN1s00GGASgjljcMxsEpJTw+/2wWq2FGSRNG7VOCyrtZpwcikLVJAQARRHoavac9rlExUBKiXA4jEAgoDfU2r59Ow4cOJCWAmGz2fQ705s2bcK+fftgs9kQDkhYrHa4PBX6MZeuugoLGipw/swGWK3WjFSLhQsXTmiMDJDzbHBwMK1bm9/vh8ViwaWXXgoAePrpp3Ho0KG057S1tekBsqZpaa2KXS4XamtrASSqPjz88MOjXhEJIdDc3Dzm+JrL7XBYjNh7MohoXEVruR3NHu6cptJ29dVX4xe/+AV+uPpKbD/q1VMsAMCgCMytdeGdd94BAHR2dhZqmDRNCCFwWUcVDgwM4bA3BJvJgFnVTpRZTad/MlGBJfsVJIPcOXPmQAiBrVu3Yvv27frj8XgcBoMBjzzyCIQQ6OnpwZ49e+B2u1FRUYG2tra0Uq033HADzGYzTCYT9vYF8O6R9Lm4srYBXbPrYDWNXs51Ihggn6VkRYdkbm13dzeOHj2atgIshMAdd9wBAHjxxRfx0Ucf6c83GAxoamrSA+QFCxags7MzLcc3tQXxrbfeOuZ4xrpdMF4VdjOWtnDFmCjpc5/7HGbNmoUv79iOy2fPxzuHTqF/KAazIVF+q91jxZpHHsH9998/Zq1tovFShEB7pQPtldyUR1NHPB6H3+/Xc3x9Ph8WL14Mq9WKrVu34k9/+hP8fj807eP84IceeghOpxOxWAyapqGhoQFz5syBy+WC2+2GlBJCCFx77bVYu3btqK/tcHz8s9BR5YCqSew84UdU1VBhN+GC5vKcBccAA+RRSSkxNDSkB7ozZ86EEALvv/8+du3alZbnK6XUr4B27NiBrVu3pqU5JFsdA8CKFSuwbNky/XM2my3tNsDSpUsLcbpENIaqqio89dRTuOaaa/DP//zPuPnmm/Vui9u3b8d1d34dZrMZX/nKVwo9VCKiMxKPx3Hq1Ck98E3+ueiii1BZWYnt27fjhRdeyHheW1sbGhoa4Ha7MWPGDD3wTf5JNlFaunTpmDHORPoZCCEwp9aFObUuPcDOtZILkEe2Kk4GukuWLIHNZtOvgJKtipOSV0Berxe9vb1wuVxoamrSA11N02AwGHDFFVfgqquuytqqGGDnKqJiddNNN6Gqqgrf+ta38OCDD2L27NkYHBxEf38/7r//fnz961/PyOsnIpoqIpEIenp69MA3GQgvX74c7e3t6OnpwU9+8pO059jtdsybNw+VlZWoq6vDypUr04Jft9ut30Hv6OhAR0dH3s9rshqFTbsAORgM4tChQxmVHFavXo2amhps27YNv/nNbzKeN3v2bNhsNv0KKDXFweVy6Rtvli9fjuXLl4/6+smav0Q0/axYsQIrVqxAd3c3enp64HA4cO655zIwJqKC0DQNqqrCZDIhGo1i69atacGvz+fDsmXLcMEFF8Dv9+NnP/sZgERQ6XK54HK5EI/HAQB1dXW46aab0laAjcaPw8RS61Uw5QPkWCym7wy32+3w+XzYtGlTxgrw2rVrMXfuXBw/fhzPPvssgI9bFbtcLr2xRUtLC9asWZNRyzeZN1ioKyAiKh4zZszAjBkzCj0MIprGkvGPEALl5eWQUuKll15KC34DgQCWLVuG1atXQ0qJdevWwWg06kFuU1MTysrKAAAejwd33323Xv5s5J4lh8MxJTqBThUFC5AjkUjaKm9lZSUaGhoQCATw/PPPp7UqBoBrrrkGS5cuRSQSwZtvvqkHthUVFWhpadE3sjU1NeHee+8dtcZdZWVloYr4ExERUYmTUiISiehBrsFg0C+4n3/+efT29sLn8yEUCgFIbN6/+eabIYTAnj179Bq/NTU1cLvdaG1tBZAo5fq1r30tY29TktFoRFNTU/5OtMgVJEA+duwY/vEf/zHtseXLl6OhoQFmsxnxeDytVXEy3xdIbJZ59NFHR805sVgsaGhomPRzICIiIkolpUwrcZZcAe7q6gIAPPvss9i/f79+VxtI3NlOBsiapsHj8egLfy6XKy2t4XQbgZnmmTsFCZAdDgdWr16dluaQXAE2m834/Oc/P+pzJysZm4iIiGgsgUAAAwMDaWkO8Xgca9asAQD8/Oc/x549e9KeU15ergfIjY2NKC8vT8vzTaZAAMAnPvGJ/J0MjakgAbLb7R5zoxsRERFRvp08eRJHjhxJK3MWCATw+c9/Hoqi4NVXX8XmzZv1rzeZTPB4PHqpsfPOOw8dHR366m9ZWVla/d5LLrmkEKdV0lRVRSAQSHtPk6v7Y1UWm/Kb9IiIiIjOhJQSoVAIFosFBoMBx44dw4cffphR6/cLX/gC3G43du3ahVdeeQUA9MpWLpcLsVgMFosF559/PubMmaM/PrKl8dy5cwt1qiUpGo1mvJcjPw4Gg5ApHfcA6HncyTbX2TBAJiIioqKT2tK4vLwcdrsdx44dw8aNG9MCpHg8jrvvvhtNTU04ceIEXnvtNb1LbbKlcTLIXbx4MebPnw+32521fGN9fX2+T7MkSSkRDoezrvqmfpws5JDKZrPpKSx1dXVpq/nJx0de2GTDAJmIiIimlNSWxj6fD/X19aiqqkJvby9++9vf6qkPyZbGt9xyC+bNm4dIJIKDBw/C7XbrLY2Tub5AoiLEOeecM2pLeKfTCafTmbfzLEWpFzbZgt7kY7FYLO15ydK9qRc2yeA3+R67XC6YzeacjJMBMp2xcDiMI0eOwGQyoampKaOkHhGli8fjCAQCGBoaKvRQiAomtcRZMjhqampCe3s7vF4vnnzySQSDwbTnXH311aiqqoLJZILZbEZ7e3tacNTY2Agg0fZ4rEoPqY0vKPdSL2xGC3z9fr9+YZNkMBj097KhoSGjXXWydvNoFzaTgf9TaMKOHj2K73znO3jmmWfgdrsRiURgtVpx33334YEHHtC7DhKVimRB/9Ta7iObGfn9fr2uKavx0HR2/PhxeL3etACpqakJXV1diMViGWVegcTmtfb2dtjtdsydOzdjVdDj8QBIVIT47Gc/m+9TImS/sBn58cgLGyBRnSz5Xs6YMSNr8Gu326fcvMgAmSZk3759WLVqFW6++WZs2bIFbW1tkFLinXfewTe/+U387ne/w0svvQSbzVbooRKdlWQx/5EBb7bANxKJZDzfYDDot2srKirQ2tqa1r3zG9/4Rv5PiugMJas0AMCePXswMDCQFhzV1tbiz/7szwAAzzzzjH6XJLWlMZDYHHX11Vfrt8qTAXByZddkMuHaa68twBmWLiklhoaGRl3xTf472zxnt9v197ChoSEj8HW73bBYLAU4q7MnRu7sy4euri6ZWiaFioOUEosXL8Y999yDe/7iPmw76sXBwRCMisCsaic6K+24/fbbUV1dje9+97uFHi5RVsld7acLegOBQEYOHJD4BZ4a6Kb+nfrv0bpZJQkhtkgpuybzXE+Hc3Hx6wtE8N5RL3zhGDw2E85t9KDSPrEczFgshlAopOfpbtu2DUePHk0LlMrLy/G5z30OAPCDH/wAx48fh9Fo1IOgtrY2XHbZZQCA/fv3w2Kx6D8PTL8rHE3TRi1xlvrveDye9rzkhc3IHN/UixqXy5V1I2OxGW0u5goyjduGDRsQiURw33334Q97+uANx6BJIAJg+1EfQjEV//RP/4QFCxbgW9/6lj7ZEuWDpmn6KsjpAl9VVTOen/oLvampadTg12KxTLlbgVSaBoai+OO+k1CHF7p6A1Gs39uHq2bXoMxqSmtpHAwG9W5tmzZtwt69e/UAKRkcP/jggwCAXbt2oaenRw+Mampq0rq5ffKTn4TZbB71IrC9vT0PZ0/J1K6xqjwEAoGMEmdGo1F/b5uamtKC3tR831K/sGGATOP261//GrfffjsGQjH4I3FoKT9zqpTY2xfEOQsbsGTJEqxfvx433HBD4QZL00ayyPvpUh2CwWDGxg/g45I/LpcLlZWVWYNep9OZs53PRPkgpcTm/cdwsrcPQ4EAQkE/2ucuhIABv3rpFQwd3gOfz6ffBRFC4NFHH4WiKPrPTGpL49RubrfccsuYm6GS+cA0OVIvbMba7JZts6/FYtGD3Jqamqyrv6e7u0UJDJBp3Px+P+bOnYtgNHP1LUEiqmqoqqpCIBDI69io+MRisVGD3tTgN9svgWS5n2SgW1dXN2rgy13rVGxUVc24Be7z+XDxxRfD4XBg06ZNWLduHfb0+hCKfXxR2NA6E3aXG3GDGXV1dZg1a1ZagJR0+eWX4/LLLx/19fNZKaDUSCnHVeIsGo1mPDeZt516YTNyM2Ox5vtORfzNQePW0tKCnTt34rY7zdCy5K6bDAqsBoGdO3fi7rvvLsAIaSrItrEtW/CbrcC7oih6kOvxeNDc3Jw135e3/6iYhcNhHDt2LCMwWrlyJerq6rBz50688MILac8xmUxYuHAhHA4H6urqsGzZMrSHFfTGFFgdTtgcLlhsdhgEcPGS8zG/jilu+ZZ6YTNWibORKV6KouiBbm1tbcaFTTL45YVLfjFApnH77Gc/i66uLjz22GOYXe3EnpNBqJqEAKAIgSUt5di0aRN8Ph8uvfTSQg+XcijZ1Wis1d7k39lWPoxGox7gVlVVYcaMGVk3tk3FUj9E4yGlhJQSiqIgFAph165dGauEq1atwpw5c3D8+HH853/+p/7cZEvjZJWA5uZmXHfddRmVAJI/Gy0tLWhpaUEkrmLdh72IqBpUTcKoCNhMBsyqZqOLXMvW0jhbibPRWhq73W60trZmTXlwOByc96YgBsg0bm1tbbj++uvxmc98Bs8++ywaymw4dGoIRoOC9koHfCdP4I477sDf/u3fcnWvSCRv950u6A0EAhm7nIFEfctkgFtfX4/Ozs6s1R3G09aTaKpKVgJI3uGIRCJ47bXXMgKmVatWYdmyZQiHw3jxxRchhEhraZzMc6+vr8edd96prwyOrARQXl6O8vLy047LYjRgzdxadA8M4VQohkqHGa3lNhg5/45brlsaj9zwxrmvsDRN0+OR7du3w+v1pr2/s2bNGvW5DJBpQp544gl85jOfwcKFC/HFL34Rl156KSLRKP7hey/g6aefxkMPPYQ77rij0MMseclf6KdLdUht1ZrKarXqq7stLS1Zg95kRQeiYpba+ctkMqGhoQFSSjz33HNplQA0TcOFF16Iq6++GgaDAW+//XZa5685c+agoaEBAFBWVoa/+qu/GrXzl8ViQVtbW07GbzQo6OSKcVbjaWns8/myljhLvbBJtjQeGfxOhxJnxSp5YROJRPRNoxs3bkRfX1/aeztjxgzceuutAIDf//73CAaDaRc2Y7UVZ4BME2KxWPDLX/4SGzZswA9+8AM8/fTTMBqNuPTSS/HGG2+MeTVGZy/Zqnis3N5kRYdsNc7tdrse+CZ3OGfL7+XET9NBaiUAn88Hs9mM+fPnA0g0szh+/HjaJtC5c+fi1ltvhRACgUAgraVxMhAGEilDf/M3fzPqyqCiKGlVISj3Ui9sxipxdrqWxnPmzMla4oz5voWTemETi8X0i8k//vGP6OnpSbuwaWxsxD333AMA2LFjB3w+X9qFTbIFOQDce++9sNvt4/79xgCZJkwIgUsvvZR5xjkUjUZPG/SmtipOlVztSK54NDY2jrqxjZM+TRdDQ0MZt0sVRdGbVfz4xz9GT09P2nMaGxv1ALmmpgYVFRVpq4IVFRX61yabYoyGt80nz+lKnPl8vqzVbUa2NC4rK8vI+eU+h8JKvbBJVilasmQJAOB///d/8f7778Pv9+sXNi6XC1/96lcBQL/gSV7YJEt3Jt1zzz1jvrcTvWhlgEw0SUa2Kh6rhu9YrYqTv7iTGzxGBr92u5053zTtnDx5Er29vWlBUSwWw6c+9SkAwG9/+1t8+OGH+tcrioK6ujo9QF60aBFmz56dUQYr6eqrr87r+dDYLY1TA+LTtTRuamrKutmNKV+FFY1G4fV6M97fa665BgaDAS+//DJef/31tOcIIXD++efDYDDoHRlHXtgkrV27dszXz/WFDwNkognK1qp4tFXf0VoVJwPckfV7U/9mMXeabmKxGIxGI4QQOHLkCLq7uzNWCr/yla/AYDDgnXfewdtvvw0AekvjsrIySCkhhMCFF16IRYsWpVUCSL1QXLx4caFOsyRpmqbPhaNteDtdS+OamhrMnDlz2rY0LnaBQCCjBbnf78f1118Pt9uNTZs24eWXX057jt1ux4oVK+ByudDW1pZW1cPlcqGsrEy/s7l06dJCnNaoGCATDUvmPY1nY9t4WhWPtbGNgS9NJ6mdvzweD8xmMw4dOoR33303LUAKhUL48pe/jPLychw4cAAvv/wyrFar/suypqYG8XgcBoMBF154IRYvXgyXy5X1YjFXm9zo9LK1NB4Z/I7W0jgZDDU3N2et7cua5oWlaRqklDAYDPB6vfjggw8y3usbb7wRzc3N6O7uxvPPPw8g/cImWdpz1qxZKCsrG/XCpqOjAx0dHQU5zzNx1gGyEKIZwDMA6gBoAJ6UUn73bI9LlCsjWxWPluow1sa21Bq+2Tq2cYWDCm2y5uKRnb8aGhrgdrtx+PBhrF+/Pi39AQDuuusutLa2wuv1Ys+ePXrTl2Tnr2Sps66uLlxwwQWjtvgeT5kzOjsjWxqPlvebbe9DstJNcuU3W5UH3gUrrHg8Dp/PB4vFAofDAZ/PhzfeeCNjI+ONN96Ic845B4ODg1i3bh2MRqP+HjY1Nem/29rb23H33XfrGxlHXtjU1NSgpqamEKc6KXKxghwH8FUp5VYhhAvAFiHE/0opd+Xg2ESjGqtVceq/x9OquL6+PmO1N/lvbmyjInFGc/Hg4GDaL8y2tjY0NDTgxIkT+NnPfpa2YQYAbrrpJpxzzjlQFAWqquotjZPBUVVVFQBgwYIFWLBgwaivy3zRyTWelsapFzapsrU0HlnpYbQLG5p8Iy9sXC4XamtrEQqF8MILL6RtgAOA1atXY/ny5dA0Ddu2bcu4sKmurgaQ2MT6ta99bdQLG4fDAYfDkddzLaSzDpCllMcAHBv+t18I8QGARgAMkGnCpJR6x6LTBb8TbVWcGvyOzFckKnZnMhcfPXoUjz/+eNpjV155JRoaGmC329PqvyZ/qSZ3jTc0NJy20gNNjtO1NE4+PrLEWWpL49QLm5EbGbkoUDipFzbJ99btdmP27NmQUuL73/8+fD5fWsfSpUuX4pprroHZbEYgEEi7sEluagQAj8eDhx9+eNTXNhqNMBqZeZuU0++EEKINwHkA3s7yuXsB3Ask2mRSaUltVXy6Fd9sKxqprYpramrQ3t6edbWXJXyIxj8X19bW4vrrr08LkJIruy6XCzfccEP+Bk0AEpUARlv1HW9L47a2NrY0nqJ8Ph8GBwfT3luXy4Vly5YBAB5//HF4vd6058ydOxezZ8+GEAItLS1p5eySNX+BROWjv/iLv8j7OU1XOQuQhRBOAM8D+IqU0jfy81LKJwE8CQBdXV2ZiZ5UlHLZqrihoWHUjW1s10k0PhOdi88777w8j7A0JavfnG7Vd7SWxslgqL6+PutmN86RU8PRo0fTurn5/X6YzWbceOONAIBf/OIXOHLkiP71JpMpbePasmXLIIRIe29T0xquu+66/J1MictJgCyEMCExIf9USvlCLo5JhaWqKoLB4GlTHYLBYNZWxTabTQ9wR2tV7HK5CpbHpmka1q1bh9/85jcIBoNobm7GXXfdxU6AVNQ4FxdGsrX7WCXOxmppnNwAPGPGjJJradzf34//+I//wLZt26AoCi6++GJ86lOfmjK5rsm7nzabDQCwf/9+9PT0pL2vqqriS1/6EgBgw4YN+OCDDwB8fGFTW1urH2/VqlWQUurv7cgLm6lW6qyU5aKKhQDwNIAPpJT/cvZDosmUbFU8no1tI2/hCSH0VsVOpxO1tbVZV3tdLteUzmPauXMnbrzxRjidTtx+++2orKzE+++/j0suuQSrV6/GU089pU+GRMWCc/HkOJuWxskgN7WlcWrgW8qbgKWU+Jd/+Rf8/d//Pa677jqsWrUK8XgcL774Ir7+9a/j3/7t33DLLbdM6hhSWxrX1dXBYDBg9+7d2Llz3s9z/QAAIABJREFUZ9r7G4/H8cgjj8BoNGL37t3YtGlTWhOn1Prcq1evxurVq0etbDRz5sxJPSfKHZGtrNWEDiDExQA2ANiBRGkhAPg/Usrfjfacrq4uuXnz5rN6XUp3uo1tY7UqVhQFDocjYyPbyL8dDkfRT+Y9PT248MIL8e1vfxu3ffrT6N/1NkL9x1ExezEMnlrccccdiMVieOGFF3i7kiaVEGKLlLIrh8fjXDxB4ylxlq0KjsViydjcNjL45X6IsX33u9/FD3/4Q6xbtw515S70bnsditGEmnMvxY4PdmPNmjV48sknce21157R8Ude2HR0dMBms2H37t3YsGFDxoVNsj73W2+9hbfeeivjfT3//PNhMpkQjUZhMBiK/nchfWy0ufisA+QzUeqT8nglS7mM1antdK2Kxwp4S7FV8f3334+Kigr834cfwut/9xmEBk4AUoPUNLRd8Ul03vJXWLRoEZ566ilccsklhR4uTWO5DpDPxHSdi5MtjU9X4iy1EkBSsqXxaIEvWxqfvUAggJaWFrzzzjuwDezH1u9/DUJRAAgIIXDR//kRNu8/jgceeAC7du3KuNAYeWHj9/sxb948VFVVYd++fXjhhRcyLmzuvPNOtLW1Ye/evdi4cWPG+9rW1sb3tUSNNhdP3fvg01hqq+LTpTpk29iWbFXscrnYqngCgsEgnn32WezcuRM7f/YdBE8chFQ//v72vPJL1F9wBb74xS/iBz/4AQNkoiko2dL4dJvdRna7TJaATNZ/7ejoyFribCqnh00Xzz77LFasWIHWxjr8/u8/AS368cZEVZP4wz/ch0VfexrRaBSPP/44ZsyYgYULF6K9vR2HDh3C008/nXHMiooKVFVVoaysDHPnzs24wEk2nuns7ERnZ2fezpWKF2eCHErmM51utXe0VsVWq1UPcEe2Kk79t9lsZuA7ATt27MATTzyB1157DUNDQ3j00UexaGAT2mzpFx9qJIxj77yMiy66Aj/+8Y8LNFqi0hWLxU5b4ux0LY2Tm4LZ0nhqSeboqqqK119/HY2NjXjhxz/AtoM++INBtJZZ0FlpQziu4cWtH2Hbv/8QHo8Hr7zyClauXKm3Fq+srMTq1aszVoCTFzbV1dVYu3ZtAc+UpgsGyOOQbFV8ulSHsVoVJyfo6urqUVMdpvNO5UKQUuLrX/86/uu//gv33XcfHnvsMdx7773o6OjA3z/2U5xfZ8O959fCoCQuNoTRCJPDjcFwmF2iiHIotQ76mbQ0TgZCI1saJx/n3bLCiv3/7d15fFTV/f/x15k1y2QP2UhYDTvIEiIUZQcFLaICVVu3ulVaEbVa61Lq2p9Yxa3aUrfautWl36pVEGVVAQFBCLIvQiCQhZB99vP7I2TMkCAIydwh83k+Hjwe5A4z874ETj5z7rnn4/Hg8XiIiYkB4Msvv2yy12/Pnj2ZOHEiSim2b99ev2+zRVNW4yIKAuNwtNXEOR0TOPfaa9lZWMTYsWO57rrrAu8VExPDsGHDjDhNEWEiukD2eDzHXeJQXV39g62KGwrcxq2Kjy5+ZTG/MWbPns2iRYsoKCjAUlPKd0v/i017ye/ZmQWvPMnPp/+W1zeUcsWZ9W02TWYLOcMn89eHH2PUqFEGpxfi9ND4ytkP3fB2dAOghjG04fJ345bGjWcG5cOqcRq3NPb5fGRmZgLw6aefcvDgwaAPNt26dePyyy8HYMWKFbjd7qCWxjk5Ofi9HopWLWBSn0xenLec+/7vHb6YtY260n3oIzfLma02+g/JJ7NTVz799FMefvhhw85fRLY2VyA316r4WLO+x2tVnJycHLhcd3TRK62Kw1tdXR2PPfYYK1eupLpgCetfegC/18PYbAu/+80NvP63p3nugTuZOOMBLu6TRmp6FgNufJiDVS5effVV1qxZY/QpCGG4xi2Nf2jpQ3MtjRuK3MYtjRsXvtLS2FiNWxo3fIDp27cvAB9//DHbt28P+mCTkZHBr371KwCKi4ubtDRuvNfvzTff3OSKqN/r5osHr6Ryz1a6OWuoLi/lkak/YeYzb7Dpzccp374eUKT3H8GAXz3CE08/TX5+vmyLJgxz2hTIDTe2HW8bsx9qVdxQ3DZuVXx04Stb87QN77//Pnl5eXTKac+8h6YFbgK5IDeRr/dXc830mbzy8TImXriB8mFn87Pf3MyiRYu47rpLefDBBwPr3YRoy7xeLzt37jzmDW81NTVNnnN0S+PmdnmQlsbGOvqDTcPe9mPGjAHgf//7H19//XXQvTAxMTGBAtlutwc+2DR8XxtucgMCM8XH0txywX0r5lG5Zws+Vx0mpbh1SAYPLdmBb9bv+MMrH2DBhzKZqa5z8ac/z+GFF15g2bJlLfHXIcRJMbxAPlar4uaK3+ZubLPb7YECt6FVcXPre+12uwzYEWTPnj307t2b6v07j2wfVM9qNnHfiGze3FTBkGHnEJeQyPr1G3jqL89htVp57LHHmDJlioHJhQid4uJiXn311cDXjVsaZ2VlNbvLg7Q0Nl51dTXFxcVNPtRMmTIFq9XKp59+yvLly4OeY7VaGTFiBBaLhQ4dOhAVFdXke9tg9OjRLZ65dMNyfK7v15h3S4nm/lE5vPrFal7o0IGhQ4fi9Xr58ssvGTduHJ9//jkdOnRo8RxCnChDCuRDhw4xd+7c47YqbihwO3bs2GybYofDIevTRLPi4uLYuHEj9qQ0/N7gvU5tZhNXD8zg+f++xfW3/g6Hw8GMGTMYPHiw/OAXESUpKYmrr746UCDJjcLGabjBWylFeXl5YGa/8ez+pZdeSnJyMhs2bGD+/PmB5zZ8sHE6nVitVnr16kW7du2C1nQ3niRqmCkOpdiMDpisNvye78fjzklRzLlqHO2v+RPr16/HZDLxwgsvBNY6C2EkQwpkn89HTExMs62KG34ve1GKU3H++edz99138+yzz5I5eBxFqz8LLLMwWe2k9swnPqsTK1asYPHixfTo0cPgxEKEXnR0tCwnCoGGGxltNht2u51Dhw6xZs2aJktbLr/8crp06UJRUREffPABSqnA3s3JycmBIrpnz55kZGSQkJDQ7AebnJwccnJyjDjVY+owagrb//cyfo8HqD8Psy2KHlNnkNa1q6w1FmFHOumJNmvatGm0b9+eP89+lG3/ncueRW+j/X6yz7mQHlNu5oGHH2HFihVBMzFChIp00msbGrc0blirW1FRwbx584L2bvb7/UyePJn+/ftTWFjIyy+/3GQJy4ABA2jXrh0ulwun09nmdkGq2reTja89Svm2dUSnZNHzZzNJHzDC6FgiwkmraRFxDh06xOjRo+nTpw933XUXffr0AWDr1q089thjLF26lCVLlpCRkWFwUhGJpEAOf0e3NE5OTqZjx47U1dXxj3/8g8rKyqBtQEePHs3w4cOpqqri1VdfbdKwpHPnzqSkpAQtpxBCGEtaTYuIk5yczNKlS3niiSc499xzsVgsmEwmamtrufbaa1m+fDnJyclGxxRCGKDxFmcNSx2Sk5Pp378/Wmtmz57dpHFJXl4eHTt2xG63k5CQQHZ2dtBNbmlpaUD9PRC//vWvj/neUhgLEf5kBllEBK/Xy549e9Bak5OTIzd3CsPJDHLrKioqory8PKgITkhIYNy4cQDMmTOHioqKwJ83mUz07duXiy66CIBFixZhs9maNC+R+2OEaFtkBlmENa01q1atYv78+TidTnJzc5k6dSqxsbEt8voWi4UuXbq0yGsJIYzh9XoDBeqOHTsoKioKKoCjoqK48sorgfpmF3v27AHq///Hx8cH3cw2fvz4QEOThr2bGzd/km6aQkQ2KZCF4bZs2cIVV1xBaWkpU6dOxeFw8N5773H77bdzzz33cOutt8olyRBxu93s3LkTv99P586diY6ONjrSaaWhoVFVVRVpaWkopdi2bRvbt28P2tP96Ev3kU5rjdPpDPy9AWzatCnQza3hl9aau+66C4C1a9dSUFBAVFRUoMhNTU0NvOaECRMwmUzExcURHR3dZAzp3bt36E5QCHHakQJZGGrnzp2MGjWKWbNmcf3111O9bzvuynJ+f8ft7Nl/gMmTJ1NbW8u9995rdNQ27fDhw8yePZsXX3yR+Ph4TCYTpaWlXHnllfz+978PFC2Ryu/3U1tbG9TEqHfv3tjtdgoKClixYkXgeENDozvuuIPY2FgKCwtZt25dYAvL7OxsHA6HwWcUOke3NO7atStWq5WCgoLAVmeNWxrffffd2Gw2CgsL2bJlC/Hx8UEtjbXWKKWYOHEikyZNOuZyKdlL9+T53E7Kt6/HGhtPfIfuMkEhIpIUyMJQ9957L9OnT+eay6ex7L5pVO/biTKb0X4f/W98hPnz59OrVy+uueYa2rdvb3TcIEVFRbz++usUFhYSHx/PJZdcQr9+/YyO9aOVlZUxcuRIBg0axNKlS+nevTsAu3fv5vHHH2fIkCEsWbIk7PZVbSlut5uSkpImnTyHDRtGSkoKGzZs4L333uPo+zUyMzPJzMzEZDJht9tJSUkJ2tO94XL+yJEj2+zl+qNbGldWVtK3b99Ao55PPvmEqqqqoGZQv/71r2nXrh1erxev1xtoadwwC9xQjI0dOzawXrg5MTExrX5+kejA14tY8+xvUcqE9vuIScth6N0vEpWQevwnC9GGyE16wjAlJSV069aNnTt3sv2VWRz4ehHa5w08brLZGTvnE26754+kp6cza9YsA9N+z+12M3PmTN58800uueQSevToQUlJCf/617/o1q0br7322mk1ezV16lQ6dOjA7P/3J7a8+yx7Fr+L9vvI/skF9Lz0Nv785NPMmzePxYsXGx31hHm9XrTWWK1WampqKCgoaNLGfuzYseTm5rJjxw7++c9/Bp6rlCI2NpYpU6bQqVMnDh48yMaNG5u0sG+YaT9Z4X6Tns/nC7rJraEQHjhwIJmZmWzdupXXX3+9yfOuuOIKunbtyp49e1izZg1xcXGBhhbx8fGkpaXJjW5hylVZzoIZowNNlQCU2UJq77MYetcLBiaLLJWF2yj4xyOU71hPdFI6PX42k6z88UbHarPkJj0RdgoKCujbty+JiYlNimMANBSt/pTx48fz8ssvGxPyKFprrrjiCmpra9m1axd27ebwzgJi03J46KGHeOSRRxg5cuRps4Xc3r17+eyzz9izZw9rn7+LA18vxO92AfDdwrepLNzG7b97gWeeeYYNGzYY0qK2MZfLFSh04+LiSElJoba2NtCUoeExp9PJhAkTOOuss6itreXjjz/GZDLhcDhwOBwkJiYGZngzMzO57LLLAp08j75ZKz09nfT0dKNOuVW5XK6gZQ6VlZXk5eXRs2dPDh48yNy5c4P+fHR0NF26dCEzM5N27doxatSoJnv92u12ADp06ECHDh2MOC1xkg6uW9JkOYX2eSktWIHP7cJssxuU7IdprTm8swB35SGSuw/EGhNndKST5jxcwuezLsdbVw1AddEu1j73O0xmKxmD2uaVqHAlBbIwjMlkClx6VUpx9LUMZVIokwWfzxc2a+AWL17M+vXrWbt2Lbs/mMv2D1/EZLGhfV6Suw/knrueY9euXTz11FPcf//9Rsc9ro8//pgLLrgAi8/JgTWf4fe4A4/5vW7Kt6/HWbyXSy+9lPfff7/VCuSGG9saCtzq6mpSUlLo0aMHXq+X5557jurqatzu7/MNGzaMcePGYTab2bNnD3FxcaSmptK5c2fi4uICS0JSUlK44447iImJafbfUUxMTGBZSaQpKytr0tK4YQ11SkoKF198cdAWZ413gUhKSmLECOmC1pYokwmaG2uVQpnCYww+mquijC8fuYbakn0oZcLv83LmL2eRM3yy0dFOyp5F7+D3uoOO+dxONr/ztBTIISYFsjBMv379KCgooKSkhKwhE9i/Yl7wwKAhc/BYPrr1DoYMGWJc0Eaef/55br75Zqp3rWfHR6/g97gDRWXZ5jVs/c/z3H777YwfP5777rsv7C8l19TUkJycjKuiDJPZGlQgA5jMFpzlB0lKSqKmpuZHvXbDzVQAu3bt4vDhw0FFcFpaWmBt7lNPPYXT6Qx6fv/+/enRowcWi4WcnByio6ODljmkpKQAYLfbmTlz5jFzmEymFtsusK1JTU3ltttuw+FwNFkuYrfbT8s19eLkZQwcxfqXgj/YmyxW0vqPwGQJz73jv3nhD1Tv3xV0BfKbF/9Iau+ziE45fZa6NagtLmwyDgM4y4sNSBPZwvunt2jTkpKSuPjii3niiSd4aNYfcFWWUbZpNcpswWSxMOg3j7O/rIL33nuPLVu2GB0XgHXr1vHAAw9QuOw1fK7ggs7vcVH4xQeM+9lM/H4/JSUlYb8WuWPHjnz44Yc4MjtDMxNEfq+HxM692bDhyUAx6/f7qa6uxuVy0a5dOwDWrFnD/v37g2aAk5OTufrqqwGYN28eBw8eBCAqKiqwLrXBeeedh8ViCVrj23CpHgg0bxAtq6ERhhAA1pg4zrrjeVY/eSs+jxPt85F0Rj8G3Piw0dGapbXm4NolaL+vyWNFqz+jy7m/MCDVqWl35tnsWzEPn+v7FuYoE6m9v58kajz5IFqPFMjCUA8//DBDhw4lPj6e2257Fl1bgbv6MHHtu7Lum/VMHTuWhx56KGy2GTObzXi9XszWqPpLkUfd5GqyWNFa4/V6T+kGrlA5//zzuemmm9i6fQe9r57F8ufvptbpps7jx4uJSTffwe4t3/L+f94ldttiVr30MDGd+5HcI4/ExERuvfVWADZv3sz+/fsDBW5aWlrQut0pU6YECuDGl+kb9O/fP2TnLIQ4ttSe+Zz7/FKqCndgiXEQk5pldKQf1LDrUdAxkylsZ7yPJ3PwOHZ/+iaHdxbgc9ZitkdjtkXR67Lb2fXpm2x55xnclYeIy+nGmdf+keRuA4yO3GZJgSwMlZmZybJly7jhhht48sknmTRpEg6Hg9WrV7N7924efvhhrrrqKqNjBgwbNowPPviA6ZddzHeL3wm629tsi6Lz+MtZvnw5SUlJgdnVcFBTU0NpaWnQDG9VVRUXXnghd999NxMnTmTKlClYM8dQuXcr2uwnsUM34vqNZET/HgxtH0tSlJloqya2Yiu5sb0584LvZ2cuv/zyH5zRCKe/CyHED1MmM/Eduhkd47iUUmQP+ymFX3zQZFlCVv6xtwgMZyazhZ/c/RIHvl7MoS1riEnLIXvYTylet4RvX5uNz1XfZKhq71aW/+laRs3+gJh24bUFalshBbIwXE5ODh9//DHbtm1jwYIFuFwuxowZw4QJE8JuDe/06dO58MILufHGGznz2vvZ8MqDaO1H+3zkjLyEjmMvZ8bkydx0002tOoOstcblcmG1WjGbzRQXFzfp1lZVVcVVV11FfHw8a9asYeHChYHnm81mHA4HTqeTGTNmsG3bNv7+979z0UUXMf6iK4iNjWXFihX07tOX/HbRXNs/GVOjAtiydTG5ubMDX8vlPiGEEfpcdQ+emgoOrluKMpmxRMUy6ObHscUlGR3tpCmTmcy8MWTmjQkc2/p/fw0Uxw38Pi/fLXqXntNmhDpiRAiv6kNEtNzcXHJzc42O8YMGDhzIZZddxrhx43jttdc4729fUFu8D3tiKhW1Ln557bVUVFQwffr0k3r9xq2Kq6urSU9Px+FwsH//fj7//POgGWCPx8N1111HdnY2+/fv55NPPsFqtQbW8GZkZASaW/Tp04f27dsHlkAc3Xr32WefZcaMGfz1r3/lmWeewe/3M3DgQF6cdQv6q3fRPk9QTm/dj7thTwghWoPFHs3gW5/BVXkIT00Fsekd63fjaGPcVYebHNNeD67DBw1IExmkQBbiR3r00UeZM2cOI0eOpGfPnvTo0YPi4mIWLlzI1KlTmTdvHlFRUUHP0VpTXV3dZIlDbm4uWVlZ7N+/n7feeiuoVTHAtGnT6NWrFx6Ph+Li4kCr4sa7OQD07t2bHj16YLfbm53NTU5OPu6+zN26deOJJ54IOlaxZwufr/k/fI0LZKVI7j7wx/61CSFEq7HHJ2OPD/+9509W+oAR7F3236DdOsz2GDIGjfmBZ4lTIZ30hDhJbrebjz76KHBzWrdu3TCbzUFLHPr27Uu/fv04dOgQTz/9dJPXmDhxIvn5+VRUVLBw4cKgwjcuLo527doRHR1twNl979vX/8zOT15D+32YLFZMFhvn3P96/c4X4qSFeyc9IUT4cFWUsewPP8NddRifx1W//d6Z55A3Y06bnDEPJemkJ8SP4PF4qK6u72SUlJSE1pqFCxcGtSquqqqiX79+TJ8+Ha/Xy0MPPQR836rY4XDg8dTPvMbHx3P++ecHbWPmcDgCa6wTEhLCdiuzXpf/luyzJ1Gy4Uts8UlkDh6HJSrG6FhB/F4PlXu2YI2NJzZdurcJIdoWe0IKox//iANfL6a2uJDkbgNIyu0fdvd/OCtKqSvZhyOrK9YYh9FxTokUyCJiaK1xu91BBa7NZgt0UXv77bc5ePAg1dXVgaYVvXr1Ytq0aSilWLduHUop4uLiSExMJCcnJ9BK12KxcOONN+JwOJq0Km54fPDgwaE94RYU36Fb2N7VXrz+C9Y8fSva78fv95LQqRdn/fZ5bI6E4z9ZCCFOEyaLjaz88UbHaJb2+1j39z+w78sPMVls+L0eeky7hTPOv8boaCetRQpkpdRLwAVAsda6T0u8phA/Vnl5OeXl5UGzvBaLhbFjxwLw8ssvs2fPnqDnZGdnBwpkq9VKWloaXbp0CSxxSE1NDfzZ22677Qc/rYd7U5C2yF1VzqonfoOv0XZ7h3cWsG7uveTf9oyByUJPxmEhhFF2ffIa+1d8FNRddsvbT5PUpQ8pPU/PyaGWmkF+BXgWeLWFXk8ItNbU1tYG2gTv2rWLvXv3Bs0Aa625/vrrAZg/fz6bN28OPN9mswUVrf3796d79+5N1vk2mDx58g/mObo41n4/lYXbMFttYbEe111dQcn6zzFZ7aSdeTZmW9Txn3SaO7BmEajg2Xrt9XBw7SL8Xg8mS9OmJG3YK8g4LCKQu7qC2uK9xKTlhMWVo4rdm6jcs4W47FwSu/Q2Ok5I7P7s3026y/rcLvYs+U9kF8ha66VKqU4t8Vqi7fP5fNTU1ASK3DPOOAOz2czGjRtZv359YAa4pqYGv9/Pfffdh9lsZtOmTXz11VdER0cHCtz4+PhA283hw4czdOjQwGM2W3AnpYEDW27nhcM7N7LyzzfVb3em/cRmdOSsO/9GdHL68Z/cCopWfcqav9yByWQGBUqZ+cm9L5PQqZcheUToyTgsIo3Wms1vP8WO/7185LK+my7nXUnPS3/4al9r8fu8rJozg9KCFfWdVtEkdx/EWb/9y2nb2e+ENbvhg0Zrf8ijtJSQrUFWSt0A3AAE1m2KtsXn8wWK28azvGeddRYOh4N169axYMECamtrabx7ysyZM0lMTKSmpobDhw8HWhU3zPA2/NkxY8Ywfvz4YzYPycoKTUtUv9fN8v93LZ7qisCxysJtrJozg+EPvhWSDI1562r4+rk78budNB6KVj15C2PmfBJ2N3G0pPSBo9jwyoNBx5TZQlr/4ZE2e3zCZCwWbcWB1Z+y8+NXgy7r7/rkNRI696L9kAkhz7NnyX8o3bgCn/v7hh6HNq9m92f/psu5v/iBZ57+Oo6eyuZ/PxW03M1sj6bDiIsNTHVqQlYga63nAnOhfmuhUL2vOHUNM7Q1NTXs2rUraBuz6upqxo4dS1ZWFt9++y3vvvtu0HNNJhPdu3fH4XCQmJhIjx49gpY3NOzmAJCfn09+fv4xc9jt9lY9zxNV+u2qoL0oAfD7qfxuM87yYqKS0kKap2zzKpTJ3OS483ApdaX723QbUnt8Enm3PMmaZ28HFNrnJS47lwE3PGR0tLAlY7FoK3Z/+laT7nI+Vx27F7xhSIG874sPmuZxOyn8/IM2XyB3PvcXHN79LUUrP8FkseL3esid/CtSex37Z3q4k10sIlRDq+KqqqrAkoWqqiq+/PLLJu2KL7jgAvr160dZWRnvvPMOUN+quKHAbdjKLCcnhwsvvDBoK7OYmJjAjg6dOnWiU6dORp1yy9F+oJlZWYMmai1RsTS7n7nfj9lu7B7KoZA+YATn/vVLKnZtxBqbQFz7LkZHEkKEwLEu32u/MZf1rTHxzR+PjWv2eFtiMlsYNH02dZfeTm1xIXHZZ4TFevBTIQVyG9NwY1vjjm0pKSnk5ORQW1vLG2+8EXjM662fBR07dixnn302Xq+X1atXB7Uqzs3NJSUlBajfpWH69OnExcURFRXV5NJ9YmIiAwYMCPk5h1pKz/wj68saUSYcWV1CPnsMkNx9ILbYeOpcdUeKd1AWKym98tt0Z6nGzFYbyd3a/r89IcT3Oo6aQvm2dUGztmZ7NB3HTDMkT5cJV1Ky4YvgZQa2aLpOuNqQPEaITk437F6cltZS27y9AYwEUpVShcAsrfWLLfHaIlhRUVGTNb4ZGRnk5eXh9/t55JFHAoVvg/z8fHJycrDb7VgsliatihvW7iYmJnL33Xcfc81qwzZokc5sszPkzr+x8s/T0V4PWmuiktqRf9uzhuRRJjM/ue9V1jx7OxW7vgUg7cxzGHjT/zMkjzCGjMMi0mQNmcChrev4buFbmCx2/F4XOcMnkz3sp4bkSe2VT9+r72Pja4/iczsxW6PoedltpJ15tiF5xKmRVtMG8/l8uFwuYmLqO5OtX7+e0tLSoBng9PT0wBZkjz/+OFVVVYHnx8TE0LdvXyZMqF9vtWTJEqKiooKWOcTFxWG1yg1LLc3v83J4xwbMNjvxHXuGxc1wnppKlNkSdp3uRFPSalqIluGsKKV6/04cmZ2JSmxndBy034e7ugJrbDwms1yoD3fSajrEPB5PoMD1eDx07doVgGXLlrF79+5AAVxbW0tGRga/+tWvAFi5ciX79+8nNjaOWAGyAAAXCUlEQVQ2UOAmJ39/mfziiy/GarUGHjObg2/OGjFiROhOMsKZzJawu6xvjW1+DZwQQrRVUQmpRCWkHv8PhogymSNmeVtbJgXyj+R2u6msrAxa4uB0Ohk9ejQA8+bNY+3atbhcrsBzYmJiuPPOOwGoqKjA6XSSlJRETk4OcXFxQQXwL37xC+x2e5NWxQ06dza+IYUQQgghRFsmBTIEdgBQSlFWVkZhYWGTdb5XXHEFVquVRYsWsXz58qDnW61WRowYgdlsJj09nf79+zdZ4tDgggsu+MEs0dFtf9cBIYQQQohw1qYLZL/fT21tLVVVVSQnJ2O329m3bx/r1q1rspfv9OnTSUlJYevWrcyfPx+o33e3ocB1u91YrVb69OlDZmZmUPFrt9sD608jYReH1rT38/fZ/v7fcVdXkDFoFD2nzcQWl2R0LCGEiBju6sNs+vdTHFj9GdbYeM746fXknDMpLO6zECJUTssCuaFVceMCt0uXLiQnJ7N3714+/vjjoFbFAFdddRWdO3fm8OHDFBQUBArcTp064XA4Ajex9evXL9DY4uhWxQDt27enffu223jBSDvn/ZNNb80JbNmzZ/F7lBasZNSfP5QbHYQQIgS038eyWZdRW7IP7fXgOlzChpfvx1NdTteJVxsdT4iQCbuqw+v1BrYyazzL27dvX7p27UpRURFz585t0hjhoosuIjk5GZvNRmxsLOnp6UGzvA3bk/Xq1YvevXsf8/1jY2OJjY1t1XMUTWmt2fLuX4L2s9Q+L66KUorXLSNj0CgD0wkhRGQo/uZzXOUlaK8ncMznqmPLe8/RZcJVMossIoYhBXJNTQ0LFiwIKoIHDhzI0KFDqaur48UXv9+602Qy4XA46NixIwAJCQmMGDGiyRrfhnbF6enp/PznPz/me8t/7vCkfV48NZVNjvt9HmpLCg1IJIQQkae2pBC/z9vkuLeuBr/Xg9na9MqqEG2RIQVyZWUlK1asCBS2qampgRvZYmNj+fnPf95sq2Ko3xFi5MiRRsQWrchksRKb2Ymaol1Bx5Uyhd1WakII0VYl5fZvdiIpNi1bimMRUQwpkDMyMrj33nub/U9oMpnIzc01IJUwWv/r7mfF7Bvx+zxorxezPYaMQaNI7NLH6Ggtzl19GL/HTVRSGlprubIhhAgLiZ17k5k/jqJVn+Jz1aHMFkwWK2de94DR0Vqc9vupO3QAmyMBs72+uZKMxaKBIQWyUkr+EYomUnoOZuSj7/PdwrdxV5aSkTeW9P5tq/GJu7qCNc/eTtm3X9Wvo9ca7ffhyOpCv1/OIrVXvtERhRARbsBNj9J+6BKKVi3AFpdCx9FTiE3vYHSsFlW8/gvWPn8Xntoq/F4PoEGZaX/WePr9cpY0XRLSalqIUFr+p+so3fRV0A0wDcy2KEY88h6OrNOnGYzf5+Xg2iVU799JfIfupPU7G3WMJjcimLSaFsIYdWVFLLx9Ij63s8ljymIlqWs/zp71LwOSnTxnRSlFK+fj93rIyBtLbFq20ZFOG9JqWgiDuavKKTtGcQzg93rYteAN+l51d+CYz+0EZQrLtX/euhqW/fFyaksK8bldmG124nO68ZN7XsFssxsdTwghmlX4xYdov6/Zx7TXQ8WujVQX7caR2an+mNb4nLWY7dFhOQFQvOELVj1xc/1VSb+fTW89Sb9r7qPDyEuMjnZaC7/vtBBtlN/rgR9YWqT9PpzlxQA4y4v58uFr+OiXeXz0y0GsmjMDT21VqKKekB0fvUzNgd34nLXg9+Fz1lL53Wa+W/SO0dGEEOKYfK46/L7mC2QAZbbgrioHYN/K+XwyfTgf3zCEeb8axu6Fb4cq5gnRfh9fP3tH/Tm5nfi9bvweF+tffjDsfmacbqRAFiJEopLSiEnLAZovks32aDIHj0VrzRcPXU3pplVovw/t83Jg7WJWPzUztIGPo2j1Z/g97qBjPreTA6sWGJRICCGOL3PwuB++Kqc1CZ16Ub79G9Y9fxeuitL6rUirD7Px1Uc4uG5p6MIeR82BPc0uFTFZrBzautaARG2HFMhChNDgmU9hi0/GHBXz/WyyyYTZHk1KjzyyhpzH4Z0FOMsPQqNLgNrroWzz6sAMcziISmzX9KAyEZWcHvowQghxghI69ST3opswWW2YbFFHjiqUpf7r/jf9CbPNzs55/8TncQU91+d2suPDl0If+hisjkR0M7Ph2u/DHp9iQKK2Q9YgCxFCce27Mv7ZRZRu+gpvbQ1+rxtneTGJXfuQ0mMwSik8tZUo1fSzqzKZ8dbVQJIBwZtxxqTrKNu0Kmj2wmy10XXiVQamEkKI4+t24Q10GD6ZQ1vXYrLZqS3Zh6J+djkqqb7zrrvqMDSzkYG7+nCI0x6bPT6J9AEjOLhuKf4jxbwyW4jN6EhC514Gpzu9SYEsRIiZLFbS+g475uPJuQOavYHEEu0gNqNja0b7UVJ75jPg17P59l+zqS3dT2xGR/pedQ8JnWRQFkKEv6ikNLLOOveYj7cfOpFDW7/G56oLHDPZ7GQNnRiKeCds4K9nU/DPRylc9n9ov4+MQWPo98s/yHa6p0gKZCFOUU1xIXuX/R8+Zx2Z+eNIzu1/Sq9niYphwPRH+fovd9bPJCuFAvJueTLs7qDOGjyOrMHjjI4hhIhw2u+n+JtlFG/4gujkDHKGT8Yen3xKr5l9ziSKVi2g9NuV9TPJJhPxOd3pOuHKFkrdMsy2KM68dhZnXjvL6ChtiuyDLMQpOLhuKaufvAW/z4v2+zBb7XQ9/xp6TJ1xyq/tqizn4LolmCxWMgaMxBId2wKJRbiQfZCFaBlaa1Y9cTMlBcvxuWox2eyYLDbO/uNrxGefemfe8h0bqNi9ibj2XUjuPkhmZtuYY43F4TUdJcRpRPv9rPvbPfjcTrTPC1rjczvZ/uFL1JUVnfLr2+OT6DB8Mtk/OV+KYyGEOIbSguWB4hjA73bhra1mw8sPtcjrJ3XtS6cx00jpkSfFcQSRAlmIk+Q8XNLsPpMms0W21xFCiBAp27w6UBx/T1O+/RtD8oi2QdYgC3GSrLHxzR7XaKJTMkOc5vTiqa3iwNeL0F4v6QNGYE+Q7YiEECcnOiUDsz066GY6QMaVE1C5dyuHtq4lOiWTtH7DUCaz0ZHChhTIQpwkiz2aTmN+xncL/x3Y6kxZrMSmdyTpFG/Ua8vKNq9h5ewb0WjQmvWvPMDA6bPJyh9vdDQhxGmo/dDz2fTWk/jcLtB+oP7Gte6X/MbgZOFLa836F/9I4efvowGTyYw9MZWz//gG9vgw2UvUYLLEQohT0PsXd9Lt4ulEJadjdSTQYfhFDLv3FVmndgza72f107fiddbgc9YeaY/qYu3zd+F1Hn2JVAghjs8SHcvwh94mY9BoLDFxxGZ0pN91D9BhxEVGRwtbpRtXUPjFB/jcTvxuJ15nDbUl+9j05p+NjhY2ZAZZiFOgTGZyJ11P7qTrjY5yWqg58B3euuomx5XJzKGta0nrd+z9oYUQ4lhi2rUn/7ZnjI5x2iha/WmTJSna5+XAmkUGJQo/MoMshAgZS4yj2SYoWvuPuaZbCCFEy7I5klAWa5PjlmiHAWnCkxTIQoiQiUpsV99Su9HArExmopPTSezSx8BkQggROTqMuAiTOXgRgdkezRk/vdagROFHCmQhREjl3fIkmYPGoMwWlMlMSs98fnKPrNsWQohQiWnXnrPu/Csx6R1QJjOW6FhyL7yRjqOnGR0tbMgaZCFESFljHOTdMge/14PWGrPVZnQkIYSIOKk98xk7Zz5eVx1mqx1lkjnTxqRAFkIYwtTM+jchhBChZbFHGx0hLLXIxwWl1HlKqS1Kqe1Kqbta4jWFEEL8ODIWCyFEyzjlAlkpZQb+AkwAegGXKaV6nerrCiGEOHEyFgshRMtpiRnkfGC71nqn1toNvAlc2AKvK4QQ4sTJWCyEEC2kJQrk9sDeRl8XHjkWRCl1g1JqtVJqdUlJSQu8rRBCiEZkLBZCiBbSEgVyc3sz6SYHtJ6rtc7TWue1a9euBd5WCCFEIzIWCyFEC2mJArkQyGn0dTawvwVeVwghxImTsVgIIVpISxTIq4BcpVRnpZQNuBR4vwVeVwghxImTsVgIIVrIKe+DrLX2KqV+A8wHzMBLWuuNp5xMCCHECZOxWAghWk6LNArRWn8EfNQSryWEEOLkyFgshBAtQ/oKCiGEEEII0YgUyEKEMb/Xg/b7jI4hhBARS2uNz+NG6yabwog2rEWWWAghWlZtyT7W/vX3lG1eg8lsIfvsSfS9+h7MtiijowkhRMTY+/n7fPvabFyVh4hKTKPPVfeQlT/O6FgiBGQGWYgw4/d5+fz+X1C2eQ1oP36vm8IvPmDd3/9gdDQhhIgYJQXLWf/CLFwVZaA1zvKDrH3uTsq3f2N0NBECUiALEWZKN67EW1sF2h845ve4KFo5D6+zxsBkkaviu8188dBVfPTLPBbdOYmDaxcbHUkI0cq2f/giPrcz6JjP7WLHR/8wKFFk01qz65PXWHDzaD66Lp/Vz9yOs7y41d5PCmQhwoynpuIYjyh8LucxHhOtpa6siM/v/zll336F11lDVeE2Vj91KyUbvjQ6mhCiFbkry5s5qnFVHgp5FgHb33+Bb994nLqyIry1VRStnM+yP1yK3+tplfeTAlmIMJPa6yz8Pm+T49GpWdjikw1IFNl2LXijyQDsczvZ/O6zBiUSQoRC1pDzMNnsQcfMtiiyhpxnUKLIpbVm2/tz8bnqvj/m9+GpqWy1K3pSIAsRZuwJKfS58m5MVjsmWxTmqBgs0Q4G/ebPKKWMjhdxaov3opuZoXCWHTAgjRAiVLqcdwUJHXpgjorBZLVjtseQlNufjiMvNjpaxNE+b7NLDP0+D3WHDrbKe8ouFkKEoU5jppHe/xwOrluK2R5N5qAxWKJjjY4VkdLOHM7BtUuCZi6U2UK7vkMNTCWEaG1mWxRn3/8Gpd+upLpwB/Edu5PcfZBMVBjAZLES1/4Mqgq3BR1XykRKj7zWec9WeVUhxCmLTsmk05ifkXP2JCmODZQ97Hzic7phtscAYLZHY3Mk0mPqLQYnE0K0NqUU7XoPofO5PyelR54Uxwbqf8ODR2bzbYDCbI8m++xJJHTs0SrvJzPIQgjxA0wWG8Nm/YsDaxZyaMvXOLI6k/2TC+RDixBChFDSGWcy5vGP2bvsfdxVh0gfOJKUHoNb7f2kQBZCiOMwmS1k5Y8nK3+80VGEECJiRSWlkTvpupC8lyyxEEIIIYQQohEpkIUQQgghhGhECmQhhBBCCCEakQJZCGE4rTV1ZQdwNdu5SgghRCj43C5qigvxedxGRzGc3KQnhDBUZeE2Vs25hbrS/aA1KT3zGHTzE9gcCUZHE0KIiLH9fy+z5Z1n6r9Qip7TZtLlvCuMDWUgmUEWQhjG73Xz5YNXUVO0C7/Hhd/rpnTTKtY8+1ujowkhRMQ4sGYRW955Bp+rrv6Xs5ZNb82heP0XRkczjBTIQgjDlG5cid8bfClPez2UfbsSd3WFQamEECKy7Jz/z6BuoQA+Vx275v/LoETGkwJZCGEYn9sJurlHFH6fJ9RxhBAiIh1dHDfwHuN4JJACWQhhmNTeQ9HaH3xQKRyZnYhKSDUmlBBCRJic4ZMx26ODjpnt0eQMn2xQIuNJgSyEMIw1xsGgmx/HbI/GEu3AEhWLPSGVvJlPGh1NCCEiRsdRU0jvPwKT1Y4l2oHJaicjbww5Z08yOpphZBcLIYShMgaO4tznl1G68SvMUdGk9MjDZJahSQghQkWZzOTdMoeqfTup2red+JxcHJmdjY5lKPkpJEQLclcfZue8f1K6cSVx2WdwxgW/JDa9g9Gxwp4lKpaMQaOMjiGEaCPKt3/Djo/+gauijKwh59Fx1CWYLDajY4W9uPZdiGvfxegYYUEKZCFaiKe2isV3XYS76hB+j5vybevY98WHnPPAm8Rln2F0PCGEiAj7Vy1g7V/uxOd2AZrDOzZQtHI+Q+95GaWU0fHEaULWIAvRQr5b9A7u6sP4j3Qg0n4fXlctm/79lMHJhBAiMmitKfjHw/U75BzZIsfnrqN8xwYObV5tbDhxWpECWYgWUr5tHX63M/ig1lTs2mhMICGEiDDa58FZXtz0uN9HxZ4tBiQSpyspkIVoIYld+mCy2o86qojr0N2QPEIIEWmU2Yo9PqWZ42ZZ6iZ+FCmQhWghHUdPxRrjQJmt9QeUCbM9ip7TbjE2mBBCRAilFL1/8TvMtqjAMZPNTnx2Lqm9zjIwmTjdyE16QrQQmyOREX/6D9s/eIHSb78irn1XciffSHx2rtHRhBAiYmQPuwB7Yio7PnwJV+Uhss46ly7nXSE36Ikf5ZQKZKXUVOCPQE8gX2stK+BFRItKbEefK35vdAwRYWQsFiJYu95DaNd7iNExxGnsVJdYFAAXA0tbIIsQQoiTI2OxEEK0oFOaQdZabwLksoUQQhhIxmIhhGhZIbtJTyl1g1JqtVJqdUlJSajeVgghRCMyFgshxPEddwZZKfUpkNHMQ/dorf97om+ktZ4LzAXIy8vTJ5xQCCGEjMVCCBFCxy2QtdZjQxFECCHEsclYLIQQoSP7IAshhBBCCNHIKRXISqmLlFKFwFDgf0qp+S0TSwghxImSsVgIIVrWqe5i8R/gPy2URQghxEmQsVgIIVqWLLEQQgghhBCiESmQhRBCCCGEaERpHfpdfpRSJcB3IX/jlpcKlBodIsQi7ZzlfNs+o865o9a6nQHvGyBj8Wkr0s4XIu+c5XxDp9mx2JACua1QSq3WWucZnSOUIu2c5Xzbvkg857Ym0r6HkXa+EHnnLOdrPFliIYQQQgghRCNSIAshhBBCCNGIFMinZq7RAQwQaecs59v2ReI5tzWR9j2MtPOFyDtnOV+DyRpkIYQQQgghGpEZZCGEEEIIIRqRAlkIIYQQQohGpEA+RUqpqUqpjUopv1IqrLYoaUlKqfOUUluUUtuVUncZnae1KaVeUkoVK6UKjM4SCkqpHKXUIqXUpiP/nm8xOlNrUkpFKaW+Ukp9c+R87zc6kzh5kTIOg4zFbZ2MxeEzFkuBfOoKgIuBpUYHaS1KKTPwF2AC0Au4TCnVy9hUre4V4DyjQ4SQF7hda90TGAL8uo1/j13AaK31mUB/4Dyl1BCDM4mT1+bHYZCxOELIWBwmY7EUyKdIa71Ja73F6BytLB/YrrXeqbV2A28CFxqcqVVprZcCh4zOESpa6yKt9ddHfl8FbALaG5uq9eh61Ue+tB75JXcsn6YiZBwGGYvbPBmLw2cslgJZnIj2wN5GXxfShv/DRjqlVCdgALDS2CStSyllVkqtA4qBBVrrNn2+ok2QsTiCyFhsLIvRAU4HSqlPgYxmHrpHa/3fUOcxgGrmWFh8whMtSynlAN4FZmqtK43O05q01j6gv1IqEfiPUqqP1joi1jmejmQcBmQsjhgyFhs/FkuBfAK01mONzmCwQiCn0dfZwH6DsohWopSyUj8gv6a1fs/oPKGitT6slFpM/TpHwwdl0TwZhwEZiyOCjMXhMRbLEgtxIlYBuUqpzkopG3Ap8L7BmUQLUkop4EVgk9b6CaPztDalVLsjsxUopaKBscBmY1MJcVwyFrdxMhaHz1gsBfIpUkpdpJQqBIYC/1NKzTc6U0vTWnuB3wDzqb9h4N9a643GpmpdSqk3gOVAd6VUoVLqWqMztbJhwBXAaKXUuiO/JhodqhVlAouUUuupLzoWaK0/NDiTOEmRMA6DjMUyFrdJYTsWS6tpIYQQQgghGpEZZCGEEEIIIRqRAlkIIYQQQohGpEAWQgghhBCiESmQhRBCCCGEaEQKZCGEEEIIIRqRAlkIIYQQQohGpEAWQgghhBCikf8PrefMQkdtLxMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "X, y = make_blobs(n_samples=40, centers=2, random_state=0)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "for i, C in enumerate([1, 100]):\n",
        "    # \"hinge\" is the standard SVM loss\n",
        "    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n",
        "    # obtain the support vectors through the decision function\n",
        "    decision_function = clf.decision_function(X)\n",
        "    # we can also calculate the decision function manually\n",
        "    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n",
        "    # The support vectors are the samples that lie within the margin\n",
        "    # boundaries, whose size is conventionally constrained to 1\n",
        "    support_vector_indices = np.where(\n",
        "        np.abs(decision_function) <= 1 + 1e-15)[0]\n",
        "    support_vectors = X[support_vector_indices]\n",
        "\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n",
        "    ax = plt.gca()\n",
        "    xlim = ax.get_xlim()\n",
        "    ylim = ax.get_ylim()\n",
        "    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),\n",
        "                         np.linspace(ylim[0], ylim[1], 50))\n",
        "    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n",
        "                linestyles=['--', '-', '--'])\n",
        "    plt.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,\n",
        "                linewidth=1, facecolors='none', edgecolors='k')\n",
        "    plt.title(\"C=\" + str(C))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
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